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摘要:
激光雷达在无人驾驶领域占据了重要地位，地面滤波是从激光雷达获取的点云数据中分离和提取地面信息的关键技术。文章首先简述了车载激光雷达(VLS)的发展及分类，并讨论了各类车载激光雷达的优缺点；然后研究了VLS地面滤波算法的发展并进行梳理分类，阐述了地面滤波精度的评估方法和评估标准，并以三种典型的算法为例进行比较分析；最后总结了当前VLS硬件和地面滤波算法的不足，并展望未来发展趋势。
Abstract:LiDAR plays an important role in the field of unmanned driving. Ground filtering is the key technology to separate and extract the ground information from the point cloud data acquired by LiDAR. Firstly, the development and classification of vehicle LiDAR scans (VLS) are introduced, and the advantages and disadvantages of all kinds of VLS are discussed. Then, the development of VLS ground filtering algorithm is studied and classified. The evaluation methods and standards of ground filtering accuracy are described, and three typical algorithms are compared and analyzed. Finally, the shortcomings of current VLS and its ground filtering algorithms are summarized, and the future development trend is prospected.
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Key words:
- LiDAR /
- ground filtering /
- intelligent driving /
- accuracy evaluation /
- prospect
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Overview: LiDAR plays an important role in the field of unmanned driving. Ground filtering is the key technology to separate and extract the ground information according to the point cloud data acquired by LiDAR. First of all, this paper briefly describes the landmark events that vehicle LiDAR scans (VLS) established its position in the field of unmanned driving. According to the classification of mechanical, mixed solid and solid LiDAR, the working principle of each type of VLS is described, and the advantages and disadvantages of each type of VLS are discussed and compared. Secondly, the development of VLS ground filtering algorithms is studied. And the existing algorithms are sorted according to the processing methods of point cloud data. The ground filtering algorithm is divided into four categories: the ground filtering algorithm based on space division, the ground filtering algorithm based on scan lines, the ground filtering algorithm based on local characteristics, and the ground filtering algorithm based on additional information. According to the principles and filtering results of different algorithms, their characteristics, advantages and disadvantages are described. In addition to the above filtering algorithms, some ground filtering algorithms are also introduced. However, the adaptability of these algorithms to VLS point cloud data needs to be further improved. The common evaluation methods and standards of ground filtering accuracy are described to effectively evaluate the filtering results of various algorithms in different situations. There are three evaluation methods of filtering results: the manual calibration method, the visual inspection method, and the random sampling method. Furthermore, there are three evaluation standards for filtering accuracy: the cross table method, the Kappa coefficient method, and the algorithm time/space complexity. In order to show the characteristics of various algorithms, typical algorithms are selected for comparison from the ground filtering algorithm based on spatial division, the ground filtering algorithm based on scan lines and the ground filtering algorithm based on local characteristics. By changing the selected value of parameters, several groups of tests are carried out for each algorithm. The filtering results are arranged in ascending order according to Kappa coefficient, and the influence of parameter changes on the results is analyzed. The accuracy evaluation criteria are used to compare and analyze the optimal filtering results. Finally, the shortcomings of existing VLS ground filtering algorithms are summarized, and the development trend of VLS and VLS ground filtering algorithms is prospected. With the development of the computer technology and machine learning technology, filtering algorithms will be more intelligent and efficient.
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图 1 点云空间划分算法。(a)局部点云；(b)网格划分；(c)体素划分
Figure 1. Space division methods of point cloud. (a) Local point cloud; (b) Gridding; (c) Voxel partition
图 2 扫描线变化示意图。(a) VLS扫描场景仿真；(b)扫描线水平投影
Figure 2. Change of scanning lines. (a) Scanning scene simulation of VLS; (b) Horizontal projection of scanning lines
图 5 三种典型地面滤波算法结果。(a)向上生长滤波算法结果；(b)相邻点连线滤波算法结果；(c)坡度值区域增长滤波算法结果；(d)向上生长滤波算法结果点云图；(e)相邻点连线滤波算法结果点云图；(f)坡度值区域增长滤波算法结果点云图
Figure 5. Filtering results of three typical ground filtering algorithms. (a) Results of growing up algorithm; (b) Results of adjacent points; (c) Results of slope-regional growth; (d) Point cloud of growing up algorithm; (e) Point cloud of adjacent points; (f) Point cloud of slope-regional growth
表 1 车载激光雷达对比表
Table 1. Comparisons of mobile LiDAR
车载激光雷达类型 工作原理 优点 缺点 厂商/研究机构代表 机械式旋转
激光雷达通过部件的机械旋转完成激光扫描 大扫描视场和高扫描效率，可承受的激光功率高 机械结构复杂，设备难以小型化，行车环境下磨损严重，使用寿命短，价格高昂 Velodyne公司(美国)
Quanergy公司(德国)
上海禾赛光电
深圳速腾聚创混合固态
激光雷达通过MEMS振镜旋转完成激光扫描 实现了一定程度的小型化，响应速度较快 接收光路复杂，使用寿命短，扫描受限于振镜的偏转范围 Msotek公司(韩国)
Innoviz公司(以色列)光学相控阵型
激光雷达通过控制合成光束的指向完成激光扫描 无惯性器件，精确稳定，方向可任意控制 需要消除旁瓣的影响，难以实现水平360°扫描 Quanergy公司(美国)
Blackmore公司(美国)闪光型
激光雷达采用单脉冲直接向各个方向漫射，利用飞行时间成像 只要一次快闪便能照亮整个场景，避免运动畸变 探测精度随距离增加明显降低，视场角受限 亚德诺半导体公司
(美国)
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表 2 交叉表
Table 2. Crosstab
滤波结果 地面点 非地面点 总和 标定数据 地面点 a b e=a+b 非地面点 c d f=c+d 总和 g=a+c h=b+d n=e+f
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表 3 滤波算法对比
Table 3. Comparisona of filtering algorithms
算法名称 算法类型 算法时间复杂度T(n) 算法空间复杂度S(n) 参数/单位 参数取值范围/步进值 最优结果 最优结果对应参数 Ⅰ类误差 Ⅱ类误差 总误差 Kappa系数 向上生长滤波算法 基于空间划分 O(n/m) O(9(m-1)) 网格长度/m 0.1~0.5/0.1 0.2 0.0153 0.3854 0.1420 0.6553 网格高度/m 0.1~0.5/0.1 0.2 高度阈值/m -1.4~-0.4/0.1 -0.9 相邻点连线滤波算法 基于扫描线 O(n) O(n) 坡度系数 0.003~0.0074 /0.0002 0.0032 0.0900 0.2113 0.1297 0.7035 高度阈值/m -1.4~-0.4/0.1 -1.1 坡度值区域增长滤波算法 基于局部特征 O(n) O(n) 坡度阈值/(°) 5~45/1.0 41 0.0675 0.1107 0.0824 0.7821 高度阈值/m -1.4~-0.4/0.1 -1.1
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